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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 顏炳郎 | zh_TW |
| dc.contributor.advisor | Ping-Lang Yen | en |
| dc.contributor.author | 雷尚勤 | zh_TW |
| dc.contributor.author | Sean Aldrich Rebuyas | en |
| dc.date.accessioned | 2025-08-18T16:10:11Z | - |
| dc.date.available | 2025-08-19 | - |
| dc.date.copyright | 2025-08-18 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-11 | - |
| dc.identifier.citation | [1] Matz, J., B. A. Lanting, and J. L. Howard. 2019. Understanding the patellofemoral joint in total knee arthroplasty. Can. J. Surg. 62(1): 57–65.
[2] Grelsamer, R. P., and C. H. Weinstein. 2001. Applied biomechanics of the patella. Clin. Orthop. Relat. Res. 389: 9–14. [3] Feller, J. A., A. A. Amis, J. T. Andrish, E. A. Arendt, P. J. Erasmus, and C. M. Powers. 2007. Surgical biomechanics of the patellofemoral joint. Arthroscopy 23(5): 542–553. [4] Berton, A., G. Salvatore, A. Orsi, J. Egan, J. DeAngelis, A. Ramappa, U. G. Longo, A. Nazarian, and V. Denaro. 2022. Lateral retinacular release in concordance with medial patellofemoral ligament reconstruction in patients with recurrent patellar instability: A computational model. The Knee 39: 308–318. [5] Thomeer, L. T., Y. C. Lin, and M. G. Pandy. 2020. Load distribution at the patellofemoral joint during walking. Ann. Biomed. Eng. 48(10): 2821–2835. [6] Felli, L., S. Lovisolo, A. G. Capello, F. Chiarlone, M. Formica, and M. Alessio-Mazzola. 2019. Arthroscopic lateral retinacular release and modified Goldthwait technique for patellar instability. Arthrosc. Tech. 8(11): e1295–e1299. [7] Wu, C. 2023. Patellar malalignment: A common disorder associated with knee pain. Biomed. J. 46(5): 100658. [8] Moatshe, G., T. R. Cram, J. Chahla, M. E. Cinque, J. A. Godin, and R. F. LaPrade. 2017. Medial patellar instability: Treatment and outcomes. Orthop. J. Sports Med. 5(4): 1–7. [9] Wheatley, M. G., M. J. Rainbow, and A. L. Clouthier. 2020. Patellofemoral mechanics: a review of pathomechanics and research approaches. Curr. Rev. Musculoskelet. Med. 13(3): 326–337. [10] Yu, Z., J. Yao, X. Wang, X. Xin, K. Zhang, H. Cai, and B. Yang. 2019. Research methods and progress of patellofemoral joint kinematics: A review. Journal of Healthcare Engineering. 2019(1): 9159267. [11] Greiwe, R. M., C. Saifi, C. S. Ahmad, and T. R. Gardner. 2010. Anatomy and biomechanics of patellar instability. Operative Tech. in Sports Med. 18(2): 62–67. [12] Smith, P. N., K. M. Refshauge, and J. M. Scarvell. 2003. Development of the concepts of knee kinematics. Arch. Phys. Med. Rehabil. 84(12): 1895–1902. [13] Panni, A. S., M. Tartarone, A. Patricola, E. W. Paxton, and D. C. Fithian. 2005. Long-term results of lateral retinacular release. Arthroscopy 21(5): 526–531. [14] Pohlig, F., U. Lenze, F. W. Lenze, I. Lazic, A. Haug, S. Hinterwimmer, and R. von Eisenhart‐Rothe. 2022. Arthroscopic lateral retinacular release improves patello‐femoral and femoro‐tibial kinematics in patients with isolated lateral retinacular tightness. Knee Surgery, Sports Traumatology, Arthroscopy 30(3): 791–799. [15] Thompson, P., and A. J. Metcalfe. 2019. Current concepts in the surgical management of patellar instability. The Knee 26(6): 1171–1181. [16] Frosch, K. H., and A. Schmeling. 2016. A new classification system of patellar instability and patellar maltracking. Arch. Orthop. Trauma Surg. 136(4): 485–497. [17] Bjelland, Ø., B. Rasheed, H. G. Schaathun, M. D. Pedersen, M. Steinert, A. I. Hellevik, and R. T. Bye. 2022. Toward a digital twin for arthroscopic knee surgery: A systematic review. IEEE Access. 10: 45029–45052. [18] Dean, M. C., J. F. Oeding, P. Diniz, R. Seil, K. Samuelsson, and ESSKA Artificial Intelligence Working Group. 2024. Leveraging digital twins for improved orthopaedic evaluation and treatment. Journal of Experimental Orthopaedics 11(4): e70084. [19] Sun, T., J. Wang, M. Suo, X. Liu, H. Huang, J. Zhang, and Z. Li. 2023. The digital twin: A potential solution for the personalized diagnosis and treatment of musculoskeletal system diseases. Bioengineering 10(6): 627. [20] Sun, T., X. He, and Z. Li. 2023. Digital twin in healthcare: Recent updates and challenges. Digital Health 9(0): 20552076221149651. [21] Sahal, R., S. H. Alsamhi, and K. N. Brown. 2022. Personal digital twin: A close look into the present and a step towards the future of personalised healthcare industry. Sensors 22(15): 5918. [22] Zheng, G., and W. Yu. 2017. Statistical shape and deformation models based 2D–3D reconstruction. Statistical Shape and Deformation Analysis 1(0): 329–349. [23] Vasiliadis, A. V., Giovanoulis, V., Maris, A., Chytas, D., Katakalos, K., Paraskevas, G., ... and Vassiou, A. (2024). Finite element analysis of the knee joint: a computational tool to analyze the combined behavior after treatment of torn ligaments and menisci in the human knee joint. SICOT-J, 10, 45. [24] Kheir, N., Salvatore, G., Berton, A., Orsi, A., Egan, J., Mohamadi, A., ... and Nazarian, A. (2022). Lateral release associated with MPFL reconstruction in patients with acute patellar dislocation. BMC Musculoskeletal Disorders, 23(1), 139. [25] Schindler, O. S., and W. N. Scott. 2011. Basic kinematics and biomechanics of the patello-femoral joint. Part 1: The native patella. Acta Orthop. Belg. 77(4): 421–426. [26] Grelsamer, R. P., and J. R. Klein. 1998. The biomechanics of the patellofemoral joint. J. Orthop. Sports Phys. Ther. 28(5): 286–298. [27] Migliorini, F., N. Maffulli, J. Eschweiler, V. Quack, M. Tingart, and A. Driessen. 2021. Lateral retinacular release combined with MPFL reconstruction for patellofemoral instability: A systematic review. Arch. Orthop. Trauma Surg. 141: 283–292.Pohlig, F., Lenze, U., Lenze, F. W., Lazic, I., Haug, A., Hinterwimmer, S., ... and von Eisenhart‐Rothe, R. (2022). Arthroscopic lateral retinacular release improves patello‐femoral and femoro‐tibial kinematics in patients with isolated lateral retinacular tightness. Knee Surgery, Sports Traumatology, Arthroscopy, 30(3), 791-799. [28] Siljander, B., Tompkins, M., and Martinez-Cano, J. P. (2022). A review of the lateral patellofemoral joint: Anatomy, biomechanics, and surgical procedures. JAAOS Global Research & Reviews, 6(7), e21. [29] Wang, C. L., Chen, J. B., and Li, T. (2021). Outcome and experience of arthroscopic lateral retinacular release combined with lateral patelloplasty in the management of excessive lateral pressure syndrome. Journal of Orthopaedic Surgery and Research, 16, 1-7. [30] Fonseca, L. P. R. M. D., E. H. Kawatake, and A. D. C. Pochini. 2017. Lateral patellar retinacular release: Changes over the last ten years. Rev. Bras. Ortop. 52: 442–449. [31] Nagamine, R., T. Otani, S. E. White, D. S. McCarthy, and L. A. Whiteside. 1995. Patellar tracking measurement in the normal knee. J. Orthop. Res. 13(1): 115–122. [32] Amis, A. A., W. Senavongse, and A. M. Bull. 2006. Patellofemoral kinematics during knee flexion‐extension: an in vitro study. J. Orthop. Res. 24(12): 2201–2211. [33] MacIntyre, N. J., N. A. Hill, R. A. Fellows, R. E. Ellis, and D. R. Wilson. 2006. Patellofemoral joint kinematics in individuals with and without patellofemoral pain syndrome. JBJS 88(12): 2596–2605. [34] Pal, S., T. F. Besier, G. S. Beaupre, M. Fredericson, S. L. Delp, and G. E. Gold. 2013. Patellar maltracking is prevalent among patellofemoral pain subjects with patella alta: an upright, weightbearing MRI study. J. Orthop. Res. 31(3): 448–457. [35] Pal, S., C. E. Draper, M. Fredericson, G. E. Gold, S. L. Delp, G. S. Beaupre, and T. F. Besier. 2011. Patellar maltracking correlates with vastus medialis activation delay in patellofemoral pain patients. Am. J. Sports Med. 39(3): 590–598.Greiwe, R. M., Saifi, C., Ahmad, C. S., and Gardner, T. R. (2010). Anatomy and biomechanics of patellar instability. Operative Techniques in Sports Medicine, 18(2), 62-67. [36] LLopis, E., and M. Padrón. 2007. Anterior knee pain. Eur. J. Radiol. 62(1): 27–43. [37] Petersen, W., A. Ellermann, A. Gösele-Koppenburg, R. Best, I. V. Rembitzki, G. P. Brüggemann, and C. Liebau. 2014. Patellofemoral pain syndrome. Knee Surg. Sports Traumatol. Arthrosc. 22(10): 2264–2274. [38] Dixit, S., J. P. Difiori, M. Burton, and B. Mines. 2007. Management of patellofemoral pain syndrome. Am. Fam. Physician. 75(2): 194–202. [39] Gaitonde, D. Y., A. Ericksen, and R. C. Robbins. 2019. Patellofemoral pain syndrome. Am. Fam. Physician. 99(2): 88–94. [40] Mizuno, Y., M. Kumagai, S. M. Mattessich, J. J. Elias, N. Ramrattan, A. J. Cosgarea, and E. Y. Chao. 2001. Q‐angle influences tibiofemoral and patellofemoral kinematics. J. Orthop. Res. 19(5): 834–840. [41] Dejour, H., Walch, G., Nove-Josserand, L., and Guier, C. H. (1994). Factors of patellar instability: an anatomic radiographic study. Knee Surgery, Sports Traumatology, Arthroscopy, 2, 19-26. [42] Ricchetti, E. T., S. Mehta, B. J. Sennett, and G. R. Huffman. 2007. Comparison of lateral release versus lateral release with medial soft-tissue realignment for the treatment of recurrent patellar instability: A systematic review. Arthroscopy 23(5): 463–468. [43] Small, N. C. 1989. An analysis of complications in lateral retinacular release procedures. Arthroscopy 5(4): 282–286. [44] Li, M., Z. Shao, Q. Liu, and G. Cui. 2023. Lateral retinacular release for treatment of excessive lateral pressure syndrome: The capsule-uncut immaculate (CUI) technique. Arthrosc. Tech. 12(11): e1991–e1996. [45] Hinckel, B. B., and E. A. Arendt. 2015. Lateral retinaculum lengthening or release. Oper. Tech. Sports Med. 23(2): 100–106. [46] Christoforakis, J., A. M. J. Bull, R. K. Strachan, R. Shymkiw, W. Senavongse, and A. A. Amis. 2006. Effects of lateral retinacular release on the lateral stability of the patella. Knee Surg. Sports Traumatol. Arthrosc. 14: 273–277. [47] Tjoumakaris, F. P., B. Forsythe, and J. P. Bradley. 2010. Patellofemoral instability in athletes: Treatment via modified Fulkerson osteotomy and lateral release. Am. J. Sports Med. 38(5): 992–999. [48] Marra, M. A., V. Vanheule, R. Fluit, B. H. Koopman, J. Rasmussen, N. Verdonschot, and M. S. Andersen. 2015. A subject-specific musculoskeletal modeling framework to predict in vivo mechanics of total knee arthroplasty. J. Biomech. Eng. 137(2): 020904. [49] Rooks, N. B., T. F. Besier, and M. T. Schneider. 2022. A parameter sensitivity analysis on multiple finite element knee joint models. Front. Bioeng. Biotechnol. 10: 841882. [50] Yan, M., T. Liang, H. Zhao, Y. Bi, T. Wang, T. Yu, and Y. Zhang. 2024. Model properties and clinical application in the finite element analysis of knee joint: A review. Orthop. Surg. 16(2): 289–302. [51] Haut Donahue, T. L., M. L. Hull, M. M. Rashid, and C. R. Jacobs. 2002. A finite element model of the human knee joint for the study of tibio-femoral contact. J. Biomech. Eng. 124(3): 273–280. [52] Beidokhti, H. N., D. Janssen, S. van de Groes, J. Hazrati, T. Van den Boogaard, and N. Verdonschot. 2017. The influence of ligament modelling strategies on the predictive capability of finite element models of the human knee joint. J. Biomech. 65: 1–11. [53] Li, F., D. Sun, Y. Song, Z. Zhou, D. Wang, X. Cen, and Y. Gu. 2025. Dynamic simulation of knee joint mechanics: Individualized multi-moment finite element modelling of patellar tendon stress during landing. J. Biomech. 112730. [54] Zhang, X., J. Duan, W. Sun, T. Xu, and S. K. Jha. 2021. A three-stage cutting simulation system based on mass-spring model. Computer Modeling in Engineering & Sciences 127(1): 117–133. [55] Chanthasopeephan, T., J. P. Desai, and A. C. Lau. 2007. Modeling soft-tissue deformation prior to cutting for surgical simulation: Finite element analysis and study of cutting parameters. IEEE Trans. Biomed. Eng. 54(3): 349–359. [56] Zhang, X., W. Zhang, W. Sun, H. Wu, and A. Song. 2022. A real-time cutting model based on finite element and order reduction. Comput. Syst. Sci. Eng. 43(1): 1–10. [57] Du, S., Y. Hu, M. Li, M. Shen, Z. Wang, and Y. Lei. 2025. Modelling and simulation of energy cutting tool for soft tissue using a novel extended finite element method. Int. J. Med. Robot. Comput. Assist. Surg. 21(2): e70052. [58] Theilen, E., A. Rörich, T. Lange, S. Bendak, C. Huber, H. Schmal, and J. Georgii. 2023. Validation of a finite element simulation for predicting individual knee joint kinematics. IEEE Open J. Eng. Med. Biol. 5: 125–132. [59] Diniz, P., Grimm, B., Garcia, F., Fayad, J., Ley, C., Mouton, C., ... and Seil, R. (2025). Digital twin systems for musculoskeletal applications: A current concepts review. Knee Surgery, Sports Traumatology, Arthroscopy. [60] Ahmadian, H., P. Mageswaran, B. A. Walter, D. M. Blakaj, E. C. Bourekas, E. Mendel, and S. Soghrati. 2022. Toward an artificial intelligence‐assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response. International Journal for Numerical Methods in Biomedical Engineering 38(6): e3601. [61] Michaud, F., A. Luaces, F. Mouzo, and J. Cuadrado. 2024. Use of patellofemoral digital twins for patellar tracking and treatment prediction: comparison of 3D models and contact detection algorithms. Frontiers in Bioengineering and Biotechnology 12(0): 1347720. [62] Aubert, K., A. Germaneau, M. Rochette, W. Ye, M. Severyns, M. Billot, and T. Vendeuvre. 2021. Development of digital twins to optimize trauma surgery and postoperative management: A case study focusing on tibial plateau fracture. Frontiers in Bioengineering and Biotechnology 9(0): 722275. [63] Okegbile, S. D., J. Cai, D. Niyato, and C. Yi. 2022. Human digital twin for personalized healthcare: Vision, architecture and future directions. IEEE Network 37(2): 262–269. [64] Chen, J., C. Yi, S. D. Okegbile, J. Cai, and X. Shen. 2023. Networking architecture and key supporting technologies for human digital twin in personalized healthcare: A comprehensive survey. IEEE Communications Surveys & Tutorials 26(1): 706–746. [65] Rivera, L. F., M. Jiménez, P. Angara, N. M. Villegas, G. Tamura, and H. A. Müller. 2019. Towards continuous monitoring in personalized healthcare through digital twins. Proc. of the 29th Int. Conf. on Computer Science and Software Engineering 29(0): 329–335. [66] Akkoul, S., A. Hafiane, O. Rozenbaum, E. Lespessailles, and R. Jennane. 2017. 3D reconstruction of the proximal femur shape from few pairs of X-ray radiographs. Signal Processing: Image Communication 59(0): 65–72. [67] Cerveri, P., C. Sacco, G. Olgiati, A. Manzotti, and G. Baroni. 2017. 2D/3D reconstruction of the distal femur using statistical shape models addressing personalized surgical instruments in knee arthroplasty: A feasibility analysis. Int. J. Med. Robotics Comput. Assist. Surg. 13(4): e1823. [68] Reyneke, C. J. F., M. Lüthi, V. Burdin, T. S. Douglas, T. Vetter, and T. E. Mutsvangwa. 2018. Review of 2D/3D reconstruction using statistical shape and intensity models and X-ray image synthesis: Toward a unified framework. IEEE Reviews in Biomedical Engineering 12(0): 269–286. [69] Heimann, T., and H.-P. Meinzer. 2009. Statistical shape models for 3D medical image segmentation: A review. Med. Image Anal. 13(4): 543–563. [70] Dalal, P., B. C. Munsell, S. Wang, J. Tang, K. Oliver, H. Ninomiya, and H. Fujita. 2007. A fast 3D correspondence method for statistical shape modeling. IEEE Conf. on Computer Vision and Pattern Recognition. 2007: 1–8. [71] Nolte, D., S. Xie, and A. M. Bull. 2023. 3D shape reconstruction of the femur from planar X-ray images using statistical shape and appearance models. Biomed. Eng. Online 22(1): 30. [72] Baka, N., B. L. Kaptein, M. de Bruijne, T. van Walsum, J. E. Giphart, W. J. Niessen, and B. P. Lelieveldt. 2011. 2D–3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models. Med. Image Anal. 15(6): 840–850. [73] Kurazume, R., K. Nakamura, T. Okada, Y. Sato, N. Sugano, T. Koyama, and T. Hasegawa. 2009. 3D reconstruction of a femoral shape using a parametric model and two 2D fluoroscopic images. Comput. Vis. Image Underst. 113(2): 202–211. [74] Fotsin, T. J. T., C. Vázquez, T. Cresson, and J. De Guise. 2019. Shape, pose and density statistical model for 3D reconstruction of articulated structures from X-ray images. IEEE Eng. Med. Biol. Soc. (EMBC) 2019: 2748–2751.Cohen, Z. A., Henry, J. H., McCarthy, D. M., Van Mow, C., and Ateshian, G. A. (2003). Computer simulations of patellofemoral joint surgery: patient-specific models for tuberosity transfer. The American journal of sports medicine, 31(1), 87-98. [75] Cohen, Z. A., J. H. Henry, D. M. McCarthy, C. Van Mow, and G. A. Ateshian. 2003. Computer simulations of patellofemoral joint surgery: Patient-specific models for tuberosity transfer. Am. J. Sports Med. 31(1): 87–98. [76] Cancienne, J. M., Christian, D. R., Redondo, M. L., Huddleston, H. P., Shewman, E. F., Farr, J., ... and Yanke, A. B. (2019). The biomechanical effects of limited lateral retinacular and capsular release on lateral patellar translation at various flexion angles in cadaveric specimens. Arthroscopy, Sports Medicine, and Rehabilitation, 1(2), e137-e144. [77] Salvatore, G., Berton, A., Orsi, A., Egan, J., Walley, K. C., Johns, W. L., ... and Nazarian, A. (2022). Lateral release with tibial tuberosity transfer alters patellofemoral biomechanics promoting multidirectional patellar instability. Arthroscopy: The Journal of Arthroscopic & Related Surgery, 38(3), 953-964. [78] Ali, A. A., S. S. Shalhoub, A. J. Cyr, C. K. Fitzpatrick, L. P. Maletsky, P. J. Rullkoetter, and K. B. Shelburne. 2016. Validation of predicted patellofemoral mechanics in a finite element model of the healthy and cruciate-deficient knee. Journal of Biomechanics. 49(0): 302–309. [79] Elias, J. J., M. J. Kelly, K. E. Smith, K. A. Gall, and J. Farr. 2016. Dynamic simulation of the effects of graft fixation errors during medial patellofemoral ligament reconstruction. American Journal of Sports Medicine. 4(0): 2325967116665080. [80] Farahmand, F., W. Senavongse, and A. A. Amis. 1998. Quantitative study of the quadriceps muscles and trochlear groove geometry related to instability of the patellofemoral joint. Journal of Orthopaedic Research. 16(1): 136–143. [81] Aalbersberg, S., I. Kingma, J. L. Ronsky, R. Frayne, and J. H. van Dieën. 2005. Orientation of tendons in vivo with active and passive knee muscles. Journal of Biomechanics. 38(9): 1780–1788. [82] Chen, T., and C. Guestrin. 2016. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. [83] Grood, E. S., and W. J. Suntay. 1983. A joint coordinate system for the clinical description of three-dimensional motions: Application to the knee. Journal of Biomechanical Engineering. 105(2): 136–144. [84] Ester, M., H. P. Kriegel, J. Sander, and X. Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD) 96(34): 226–231. [85] Camomilla, V., A. Cereatti, G. Vannozzi, and A. Cappozzo. 2006. An optimized protocol for hip joint centre determination using the functional method. Journal of Biomechanics. 39(6): 1096–1106. [86] Farahmand, F., M. N. Tahmasbi, and A. A. Amis. 2004. The contribution of the medial retinaculum and quadriceps muscles to patellar lateral stability—an in-vitro study. The Knee 11(2): 89–94. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98704 | - |
| dc.description.abstract | 本研究建立一套病患專屬的膝關節數位分身系統，用以預測髕股關節在韌帶鬆解術後的運動反應，並著重於外側支持帶鬆解。此數位分身系統整合了新穎的二維至三維重建方法、統計形狀建模（SSM）、有限元素（FE）分析以及機器學習，旨在提升針對髕股關節不穩定與運動異常（maltracking）的手術技術。子集式配準（SBR）為一種新穎方法，可將三維模型與已標註之二維X光影像對齊。本研究結合SBR與SSM形變技術，以重建病患專屬的三維解剖模型。並以屍體實驗作為驗證依據。研究結果顯示，針對性鬆解比起擴大鬆解範圍更能有效改善運動異常。在膝關節彎曲小於60°時，對上外側支持帶（LR）進行約25%的部分鬆解，所達成的髕骨偏移修正效果幾乎與正常狀況一致；反之，進行較大範圍或完全（75–100%）的LR鬆解則導致過度修正。若同時搭配施行25–50%的內側支持帶（MR）鬆解，則可進一步改善髕骨的偏移與傾斜校正。屍體實驗亦支持有限元素模擬的預測結果，顯示若進行雙側完全鬆解，會導致髕骨傾斜活動度增加，指出關節不穩定的風險。然而，髕骨偏移的量測結果與正常運動軌跡吻合度不佳，主因可能為骨標記的遺失所致。在未進行鬆解的情況下，髕骨屈曲延遲達30–50%；在LR鬆解情境下則達60–75%，皆顯著低於常見的正常值，顯示實驗中存在誤差。在有限元素分析中，LEPL束，尤其是上外側髁髕韌帶（sLEPL），對於恢復正常髕骨傾斜具有良好效果。機器學習分析亦指出，sLEPL是影響髕骨傾斜與偏移最具影響力的參數，具有應用於針對性鬆解決策輔助的潛力。本研究成功建立並驗證了病患專屬數位分身系統中各個獨立模組於髕骨運動模擬中的可行性，提供術前外側支持帶鬆解決策之輔助依據。未來若能納入更多術前與術後的臨床資料，將有助於提升此數位分身系統之普遍適用性。 | zh_TW |
| dc.description.abstract | This study created a patient-specific digital twin system of the knee joint to predict the kinematic response of the patellofemoral joint following ligament release, focusing on lateral retinacular release. The digital twin aimed to improve the surgical technique for patellofemoral instability and maltracking by integrating a novel 2D–3D reconstruction method, statistical shape modeling (SSM), finite element (FE) analysis, and machine learning. Subset-based registration (SBR) is a novel method to align 3D models to annotated 2D X-rays, used here with SSM deformation to reconstruct a patient-specific 3D model. A cadaver study served as a validation benchmark. Results showed that targeted release corrected maltracking better than increasing release extent. Partial (~25%) release of the superior lateral retinaculum (LR) achieved shift correction nearly identical to normal below 60° flexion. Larger or full LR release (75–100%) caused over-correction. Combining 25–50% medial retinacular (MR) release with LR release improved shift and tilt correction. Cadaver study results supported FE predictions in that full bilateral release increased patellar tilt mobility, indicating instability. Shift measurements poorly matched normal tracking due to bone marker loss. Patellar flexion lagged 30–50% in no-release and 60–75% in LR release—lower than commonly observed normal values, indicating experimental inaccuracies. The LEPL bundle, particularly the superior lateral epicondylopatellar ligament (sLEPL), showed good restoration of normal patellar tilt in the FE analysis. Machine learning analysis also identified sLEPL as the most influential parameter affecting patellar tilt and shift—useful for targeted release decision support. This study successfully developed and validated the individual systems of the patient-specific digital twin system for patellar tracking for preoperative lateral retinacular release decision support. Large clinical datasets of preoperative and postoperative data could improve the generalizability of the digital twin system. | en |
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| dc.description.provenance | Made available in DSpace on 2025-08-18T16:10:11Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgements i
Chinese Abstract ii Abstract iii Table of Contents iv List of Abbreviations xi Explanation of Symbols xiii Chapter 1: Introduction 1 1.1 Background 1 1.2 Research Problem 5 1.3 Research Objectives 6 Chapter 2: Review of Related Literature 8 2.1 Patellofemoral Joint Biomechanics 8 2.1.1 Biomechanical Role of the Patella 8 2.1.2 Patellar Tracking Mechanics and Contact Dynamics 9 2.1.3 Patellofemoral Stability: Anatomical and Biomechanical Factors 10 2.1.4 Patellar Tracking Patterns in Cadaveric Knee Studies 12 2.2 Patellar Malalignment and Maltracking 14 2.2.1 Association of Patellar Maltracking with Patellofemoral Pain Syndrome (PFPS 14 2.2.2 Definitions and Characteristics of Patellar Malalignment and Maltracking 16 2.2.3 Biomechanical Risk Factors and Muscular Influence 17 2.2.4 Influence of Soft Tissue and Bone Morphology 18 2.2.5 Variability and Diagnosis Challenges 19 2.3 Anatomical and Measurement Frameworks for Patellar Kinematics 23 2.3.1 Anatomical Coordinate Systems 23 2.3.2 Influence of Loading and Reference Frames 25 2.3.3 Imaging Modalities and Evolution of Kinematic Modeling 26 2.4 Surgical Treatment Strategies for Patellar Instability 27 2.4.1 Diagnostic Criteria for Patellar Instability 27 2.4.2 Lateral Retinacular Release (LRR): Overview 28 2.4.3 Lateral Retinacular Release (LRR): Surgical Procedure 30 2.4.4 Lateral Retinacular Release (LRR): Postoperative Changes 34 2.4.5 Lateral Retinacular Release (LRR): Considerations 35 2.5 Computational Modeling Approaches in Knee Biomechanics 36 2.5.1 Ligament and Bone Modeling Strategies 36 2.5.2 Contact Mechanics and Joint Constraints 39 2.5.3 Soft Tissue Mechanics and Load Response 39 2.5.4 Soft Tissue Modeling and Cutting Simulation 40 2.5.5 Model Validation and Clinical Use 43 2.5.6 Cadaver Study vs Simulation 44 2.5.7 Use Cases and Gaps in FEM Studies 44 2.6 Patient-Specific Modeling and Digital Twins in Orthopedics 45 2.6.1 Concept and Applications 45 2.6.2 Training and Surgical Planning 47 2.6.3 Data and Imaging for Digital Twin 47 2.6.4 FE Modeling in Digital Twin 49 2.6.5 Multibody Dynamics Modeling in Digital Twin 50 2.6.6 Digital Twins in Personalized Medicine and Precision Healthcare 50 2.6.7 Applications and Capabilities of Digital Twins 52 2.6.8 Implementation Requirements and Challenges 53 2.7 Methods for 2D-3D Shape Reconstruction in Patient-specific Modeling 54 2.7.1 2D-3D Reconstruction Methods and Challenges 54 2.7.2 Statistical Shape Models (SSMs) Fundamentals 59 2.7.3 Modern Techniques for SSM-based 2D-3D Registration 60 2.7.4 3D Bone Reconstruction from 2D X-ray Imaging 61 2.8 Computational Surgical Simulations Using Patient-Specific Models 63 2.8.1 Computational Modeling in Patellofemoral Surgery 63 2.8.2 FE Modeling for Patellofemoral Joint 65 2.8.3 FE Modeling Limitations 68 Chapter 3: Materials and Methods 68 3.1 Digital Twin System Overview 68 3.2 Data 71 3.3 Subset-Based Registration 73 3.4 Statistical Shape Model Development 79 3.5 SBR-Driven SSM Deformation 81 3.6 Finite Element Model 84 3.6.1 Model Geometry 84 3.6.2 Joint Contact and Soft Tissue Approximation 85 3.6.3 Ligament Modeling 86 3.6.4 Muscles Representation 89 3.6.5 Solver Configuration 91 3.6.6 Normal Patellar Tracking Definition 92 3.6.7 Simulation of Deviations from Normal Patellar Tracking and Surgical Release 93 3.7 Predictive Models 94 3.7.1 Data Preparation 94 3.7.2 Malalignment Predictor Model 95 3.7.3 Surgical Release Predictor Model 97 3.7.4 Model Limitations and Assumptions 98 3.7.5 Predictive Model Implementation 99 3.8 Cadaver Study 100 3.8.1 Cadaver Experiment Setup 100 3.8.2 Surgical Release 101 3.8.2.1 Objectives 101 3.8.2.2 Order of Procedures 102 3.8.2.3 Measurement Limitations 103 3.8.2.4 Kinematic Data Collection 104 3.8.2.5 Preparation for Digitization of Exposed Bony Surfaces 104 3.8.3 Point Cloud Data Acquisition 104 3.8.3.1 Equipment and Tracking Setup 105 3.8.3.2 Digitization Technique 105 3.8.3.3 Femur Data Acquisition 106 3.8.3.4 Tibia Data Acquisition 106 3.8.3.5 Patella Data Acquisition 107 3.8.3.6 Ensuring Digitization Accuracy and Surface Coverage 107 3.8.4 Coordinate Transformations 107 3.8.4.1 Patella Data Acquisition Challenges 109 3.8.4.2 Handling Marker Loss and Its Impact on Patellar Kinematics 111 3.8.5 ACS Determination 111 3.8.5.1 Femur ACS 112 3.8.5.2 Tibia ACS 113 3.8.5.3 Patella ACS 115 3.8.6 Hip Joint Center Approximation 117 Chapter 4: Results and Discussion 119 4.1 SSM Variance Explanation and Model Interpretation 119 4.2 Quantitative and Qualitative Evaluation of SBR-Driven Shape Reconstruction 122 4.3 Simulation Results: Patellar Kinematics 124 4.3.1 FE Model: Baseline Patellar Tracking of a Normal Knee 124 4.3.2 FE Model: Effect of Ligament Tightness on Patellar Kinematics 127 4.3.3 FE Model: Evaluation of Surgical Release Scenarios 128 4.3.3.1 Qualitative Evaluation 129 4.3.3.1 Quantitative Evaluation 133 4.4 Cadaver Study: Patellar Tracking 135 4.5 Machine Learning Model Results 140 4.5.1 Performance Evaluation: Malalignment Predictor Model 140 4.5.2 Performance Evaluation: Surgical Release Predictor Model 145 4.5.3 SHAP and Feature Importance Interpretation 152 4.5.4 Model-Based Personalization and Surgical Planning for Patellar Malalignment 160 4.6 Supporting Results: Point Cloud Transformations 163 4.6.1 Transformed Femur Point Cloud 163 4.6.2 Transformed Tibia Point Cloud 164 4.6.3 Transformed Patella Point Cloud 165 Chapter 5: Conclusions 167 5.1 Conclusions 167 5.2 Recommendations 170 References 172 Appendices 179 A. FE Model: Surgical Release Scenarios 179 A.1. Surgical Release Scenario: Incremental LR Release (Superior to Inferior) 179 A.2 Surgical Release Scenario: Incremental LR Release (Inferior to Superior) 182 A.3 Surgical Release Scenario: Incremental LR Release (Superior to Inferior) with Full LPFL Release 185 A.4 Surgical Release Scenario: Incremental LPFL Release (Superior to Inferior) with Full LR Release 189 A.5 Surgical Release Scenario: Incremental LPFL Release (Inferior to Superior) with Full LR Release 192 A.6 Surgical Release Scenario: Incremental MR Release (Inferior to Superior) with Full LR Release 196 A.7 Surgical Release Scenario: Incremental LPFL Release (Superior to Inferior) 199 A.8 Surgical Release Scenario: Incremental MPFL Release (Superior to Inferior) with Full LPFL Release 202 A.9 Surgical Release Scenario: Incremental MR Release (Superior to Inferior) 205 A.10 Surgical Release Scenario: Incremental MR Release (Inferior to Superior) 208 A.11 Surgical Release Scenario: Incremental MPFL Release (Superior to Inferior) 211 B. Full Surgical Scenario Static Evaluation Figures 214 C. Full Surgical Scenario Dynamic Evaluation Table 216 D. Questions and Suggestions Raised During the Oral Defense 219 | - |
| dc.language.iso | en | - |
| dc.subject | 數位分身 | zh_TW |
| dc.subject | 髕股關節 | zh_TW |
| dc.subject | 二維–三維重建 | zh_TW |
| dc.subject | 統計形狀建模 | zh_TW |
| dc.subject | 有限元素分析 | zh_TW |
| dc.subject | 外側支持帶釋放 | zh_TW |
| dc.subject | lateral retinacular release | en |
| dc.subject | finite element analysis | en |
| dc.subject | statistical shape modeling | en |
| dc.subject | 2D–3D reconstruction | en |
| dc.subject | patellofemoral joint | en |
| dc.subject | digital twin | en |
| dc.title | 應用病患專屬數位分身模擬髕骨運動軌跡： 外側支持帶鬆解術之影響 | zh_TW |
| dc.title | Simulation of patellar tracking using a patient-specific digital twin: Effects of lateral retinacular release | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 王建凱 | zh_TW |
| dc.contributor.coadvisor | Chien-Kai Wang | en |
| dc.contributor.oralexamcommittee | 吳筱梅;洪碩穗 | zh_TW |
| dc.contributor.oralexamcommittee | Hsiao-Mei Wu;Shuo-Suei Hung | en |
| dc.subject.keyword | 數位分身,髕股關節,二維–三維重建,統計形狀建模,有限元素分析,外側支持帶釋放, | zh_TW |
| dc.subject.keyword | digital twin,patellofemoral joint,2D–3D reconstruction,statistical shape modeling,finite element analysis,lateral retinacular release, | en |
| dc.relation.page | 225 | - |
| dc.identifier.doi | 10.6342/NTU202503673 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-08-13 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 生物機電工程學系 | - |
| dc.date.embargo-lift | 2025-08-19 | - |
| 顯示於系所單位： | 生物機電工程學系 | |
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